Model-based testing can be hampered by the fact that a model depicting the system as designed does not necessarily correspond to the product as it is during development. Tests generated from such a model may be impossible ...

Methods for learning decision rules are being successfully applied to many problem domains, in particular when understanding and interpretation of the learned model is necessary. In many real life problems, we would like ...

Access requests to keys stored into a data structure often exhibit locality of reference in practice. Such a regularity can be modeled, e.g., by working sets. In this paper we study to what extent can the existence of ...

Access requests to keys stored into a data structure often exhibit locality of reference in practice. Such a regularity can be modeled, e.g., by working sets. In this paper we study to what extent can the existence of ...

Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to ...

We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a single classifier as well as in weighting ...

Monte Carlo option pricing algorithms are well suited to distributed computing because simulations can be run on different computational units with no need for communication between these tasks. In this paper we investigate ...

This paper studies how useful the standard 2-norm regularized SVM is in approximating the 1-norm SVM problem. To this end, we examine a general method that is based on iteratively re-weighting the features and solving a ...